RGB-to-Hyperspectral Imaging Beyond 700 Nm With Pixel Tracking
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Solution Overview
Problem
Current imaging technologies face limitations in transforming RGB images to hyperspectral data, particularly beyond 700 nm, and lack affordable, high-resolution ground-based systems for applications like plant phenotyping and precision agriculture, which are hindered by high costs and bulky hardware.
Innovation Solution
A system and method for extracting full-range hyperspectral data from RGB images using low-cost, high-resolution ground-based devices with artificial intelligence, enabling conversion beyond 700 nm and providing accurate plant phenotyping and crop parameter measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hyperspectral imaging systems are used to capture full spectral range data, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent creates a computational copy of hyperspectral data from RGB images. Instead of physically capturing full spectral data with complex hardware, the system uses AI models to generate synthetic hyperspectral representations from standard RGB inputs, achieving spectral information extraction without specialized imaging equipment
Solution Approach 2:
The patent replaces mechanical/optical hyperspectral imaging systems with an computational approach. The physical spectral decomposition hardware is substituted by machine learning algorithms that mathematically reconstruct spectral signatures from RGB color data, eliminating the need for complex optical components
2Measurement precision
If hyperspectral imaging systems are deployed for plant phenotyping applications, then measurement precision is improved, but device portability and operational flexibility deteriorate due to bulky hardware
Solution Approach 1:
The system creates portable computational models that can be deployed on mobile devices. The hyperspectral analysis capability is copied into software form that runs on smartphones and tablets, enabling field-based crop monitoring without carrying heavy specialized equipment
Solution Approach 2:
The patent makes standard RGB cameras universal for multiple applications. By enabling hyperspectral analysis through AI processing, ordinary camera phones and digital cameras can perform specialized agricultural monitoring tasks, eliminating the need for dedicated hyperspectral imaging devices
3Quantity of substance
If conventional RGB to hyperspectral transformation methods are used, then spectral data is obtained, but accuracy deteriorates for wavelengths beyond 700 nm
Solution Approach 1:
The patent transforms the transformation parameters by using advanced neural network architectures that learn non-linear mappings between RGB and spectral spaces. The system changes the mathematical parameters of the conversion process to account for the non-linear relationship between visible light and near-infrared spectral regions, improving accuracy beyond 700 nm
Solution Approach 2:
The system performs preliminary training with extensive spectral reference data before deployment. By pre-training the AI models with ground truth spectral measurements across the full spectral range including beyond 700 nm, the system learns accurate transformation parameters in advance, improving prediction accuracy when converting RGB images to hyperspectral data
Data Source
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AI summary
A system and method for extracting a full range hyperspectral data from one or more RGB images. The method encompasses pre-processing, the one or more RGB images. Further the method encompasses estimating, an illumination component associated with each pre-processed RGB image. The method thereafter comprises removing, the illumination component from the each pre-processed RGB image. Further the method encompasses tracking, a trajectory of pixel(s) over frame(s) associated with the each pre-processed RGB image. The method then leads to identifying, a position of the pixel(s) in one or more adjacent frames of the frame(s) based on a patch defined around said one or more pixels. Thereafter the method encompasses extracting, the full range hyperspectral data from the each pre-processed RGB image based on at least one of the removal of the illumination component, the trajectory of the pixel(s) and the position of the pixel(s).